Fetching the paper…
Reading the bibliography…
Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data.
A mathematical theory of communication
Claude E Shannon · 1948
Earlier work this paper cites.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
Earlier work this paper cites.
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2008
Earlier work this paper cites.
Attribute and simile classifiers for face verification
Neeraj Kumar, Alexander C Berg, Peter N Belhumeur, and Shree K Nayar · 2009
Earlier work this paper cites.
Theano: a cpu and gpu math expression compiler
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio · 2010
Earlier work this paper cites.
Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook
Nicolas Pinto, Zak Stone, Todd Zickler, and David Cox · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
Earlier work this paper cites.
Privacy-preserving deep learning via additively homomorphic encryption
Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al · 2017
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Earlier work this paper cites.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
Cited alongside, same era.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Cited alongside, same era.
Robust large margin deep neural networks
Jure Sokolić, Raja Giryes, Guillermo Sapiro, and Miguel RD Rodrigues · 2017
Cited alongside, same era.
Yerbabuena: Securing deep learning inference data via enclave-based ternary model partitioning
Zhongshu Gu, Heqing Huang, Jialong Zhang, Dong Su, Hani Jamjoom, Ankita Lamba, Dimitrios Pendarakis, and Ian Molloy · 2018
Cited alongside, same era.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
Later among the works it cites.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Later among the works it cites.
Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
Later among the works it cites.
Efficient and private federated learning using tee
Fan Mo and Hamed Haddadi · 2019
Later among the works it cites.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz · 2018
Cited alongside, same era.
Sensitivity and generalization in neural networks: an empirical study
Roman Novak, Yasaman Bahri, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Where is the information in a deep neural network?
Alessandro Achille, Giovanni Paolini, and Stefano Soatto · 2019
Cited alongside, same era.
On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang · 2019
Cited alongside, same era.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Cited alongside, same era.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Later among the works it cites.
On the information bottleneck theory of deep learning
Andrew M Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D Tracey, and David D Cox · 2019
Later among the works it cites.
Beyond inferring class representatives: user-level privacy leakage from federated learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2019
Later among the works it cites.
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Later among the works it cites.
Finite versus infinite neural networks: an empirical study
Jaehoon Lee, Samuel S Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, and Jascha Sohl-Dickstein · 2020
Closest in time.
Darknetz: towards model privacy at the edge using trusted execution environments
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, and Hamed Haddadi · 2020
Closest in time.
A framework for evaluating gradient leakage attacks in federated learning
Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu · 2020
Closest in time.
A theory of usable information under computational constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, and Stefano Ermon · 2020
Closest in time.